OPTIMIZACIJSKA METODA ZA NADZOR NAPETOSTNIH NIVOJEV IN IZGUB Z UPOŠTEVANJEM OPTIMALNE IMPLEMENTACIJE RAZPRŠENE PROIZVODNJE S POMOČJO NEVRONSKIH MREŽ IN GENETSKIH ALGORITMOV
Povzetek
V članku je predstavljena metoda za zmanjšanje izgub v sistemu in regulacijo napetostnih nivojev z implementacijo razpršenih proizvodnih kapacitet na primernih terminalih distribucijskega sistema. Izgube delovne moči so določene z uporabo Umetne Nevronske Mreže (UNM), kjer je uporabljena sočasna formulacija v procesu odločanja na osnovi nadzora napetostnih nivojev in injiciranih moči. Ustrezne inštalirane moči razpršene proizvodnje in primerni terminali za izkoriščanje razpršene proizvodnje so izbrani na osnovi Genetskih Algoritmov (GA) izvedenih na poseben način, ki ustreza nalogam v procesu odločanja. Podatki za Umetno Nevronsko Mrežo so pridobljeni na osnovi simulacije pretoka energij v programskem paketu ''DIgSILENT PowerFactory'' na delu Hrvaškega distibucijskega omrežja. Simulacije izgub delovne moči in napetostnih razmer so izvedene za različne obratovalne scenarije, v katerih je testiran model ''vzratnega učenja'' umetne nevronske mreže za predvidevanje izgub moči in napetostnih nivojev za vsak sistemski terminal. Genetski algoritem je uporabljen za določitev optimalnega terminala za umestitev razpršene proizvodnje.
Prenosi
Literatura
S. Biswas, S.K. Goswami, A. Chatterjee: Optimum distributed generation placement with voltage sag effect minimization, Energy Conversion and Management, Elsevier, 2012.
K.S. Swarup, P.S. Subash: Neural network approach to voltage and reactive power control in power systems, Intelligent Sensing and Information Processing, International conference, 2005,
S. Sumathi, P. Surekha: Computational Intelligence Paradigm – Theory and applications using Matlab®, CRC Press, Taylor & Francis Group, 2010.
A.A. Abour El-Ela, Sm. M. Allam, M.M. Shatla: Maximal optimal benefits of distributed generation using genetic algorithms, Electric Power System Research, vol.80, 2010., pp.869–877
E. Rezenia, S.M. Shahidehpur: Real Power loss minimization using interior point method, Electric Power and Energy Systems, vol.23, 2001, pp. 45–46
K. Byeong-Gi , R. Dae-Seok: Optimal Voltage Regulation Method for Distribution Systems with Distributed Generation Systems Using the Artificial Neural Networks, JEET, vol. 8, No 742, 2013.
H. Gavin: The Levenberg-Marquardt method for nonlinear least squares curve fitting problem, Duke University, 2011.
DIgSILENT PowerFactory User’s Manual, Vol. I & 2, Edition 1, DIgSILENT GmbH, 2011.
D. Lukman, T.R. Blackburn: Modified Algorithm Of Load Flow Simulation For Loss Minimization In Power Systems, IEEE/PES, Sydney, 2003.
Y. Alinejad-Beromi, M. Sedighizadeh, M.R. Bayat, M.E. Khodayar: Using genetic algorithm for distributed generation allocation to reduce losses and improve voltage profile, International Universities Power Engineering Conference - UPEC. 2007. pp. 954–959
J.M. Abdallah, A.R. Al-Zyoud: Voltage and Reactive Power Control Simulations Using Neural Networks, IJSSST, vol.10, No.4, pp. 64–72
M. Vardarajan, K.S. Swarup: Differential evolutionary algorithm for optimal reactive power dispatch, Electric Power and Energy Systems, vol. 30, 2008, pp. 435–441
S.M. Moghaddas-Tafreshi, H.A. Zamani, S.M. Hakimi: Optimal sizing of distributed resources in micro grid with loss of power supply probability technology by using breeding particle swarm optimization, J. Renewable Sustainable Energy 3, 2011
A.Q.H. Badar, B.S. Umre, A.S. Junghare: Reactive power control using dynamic Particle Swarm Optimization for real power loss, Electric Power and Energy Systems, vol.41, 2012., pp. 133–136.
S. M. Hakimi, S. M. Moghaddas-Tafreshi, H. HassanzadehFard: Optimal sizing of reliable hybrid renewable energy system considered various load type, J. Renewable Sustainable Energy 3, 2011
S.M. Moghaddas-Tafreshi, Elahe Mashhour: Distributed generation modeling for power flow studies and a three-phase unbalanced power flow solution for radial distribution systems considering distributed generation, Electric Power Systems Research, Volume 79, Issue 4, p.680–686, 2009.
W-S. Tan, M.Y. Hassan, M.S. Majid, H.A. Rahman: Optimal distributed renewable generation planning: a review of different approaches, Renewable and Sustainable Energy Reviews 18, pp. 626–645.
R.K. Singh, S.K. Goswami: Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction, and voltage improvement including voltage rise issue, Electric Power and Energy Systems, vol. 32, 2010., pp.637–644
N-C. Yang, T-H. Chen: Evaluation of maximum allowable capacity of distributed generations connected to a distribution grid by dual genetic algorithm, Energy and Buildings, vol. 43, 2011., pp. 3044–3052.
M.R. AlRashidi, M.F.AlHajri: Optimal planning of multiple distributed generation sources in distribution networks: A new approach, Energy Conversion and Management, vol.52, 2011., pp. 3301–3308.
